Xenograft Models for Preclinical Assessment of Anticancer Therapies: A Comprehensive Review
Keywords:
Cancer, anticancer therapies, preclinical models, cell line-derived xenografts (CDX), patient-derived xenografts (PDX).
Abstract
Introduction: Xenograft models play a pivotal role in preclinical studies for assessing the efficacy of anticancer medications. In this comprehensive review, we present an overview of current advancements and future prospects in xenograft research, focusing on their significance in guiding drug development and clinical translation. Aim: Our aim is to conduct an in-depth review of xenograft models, their utility in evaluating anticancer drug effectiveness and ultimately improve patient outcomes. Methods We conducted an in-depth literature search using databases such as ScienceDirect, Google Scholar and PubMed with keywords including "xenograft model, cancer CDX PDX." We then reviewed and analyzed relevant studies that utilized xenograft models in order to highlight key findings and contributions made through such models. Results: Our analysis showcases the essential role of xenograft models in assessing the efficacy of anticancer drugs. We discuss the benefits and limitations of these models, emphasizing their importance in guiding drug development and clinical decision-making. Conclusion: Xenograft models remain invaluable tools in preclinical cancer research despite their inherent limitations, with researchers continually striving to refine and enhance these models to ensure their reliability in an ever-evolving field of cancer therapeutics. Utilizing xenograft models allows researchers to evaluate anticancer drug activity more accurately while striving for improved patient outcomes.
Downloads
Download data is not yet available.
References
Abdolahi, S., Ghazvinian, Z., Muhammadnejad, S., Saleh, M., Aghdaei, H., & Baghaei, K. (2022). Patient-derived xenograft (PDX) models, applications and challenges in cancer research. Journal of Translational Medicine , 20(1), 206. doi:https://doi.org/10.1186/s12967-022-03405-8
American Cancer Society. (2018). Cancer Facts & Figures 2018. Atlanta: American Cancer Society.
Bachelard, C., Coquan, E., du Rusquec, P., Paoletti, X., & Le Tourneau, C. (2021). Risks and benefits of anticancer drugs in advanced cancer patients: A systematic review and meta-analysis. EClinicalMedicine, 40(2021), 101130. doi:https://doi.org/10.1016/j.eclinm.2021.101130
Bokacheva, L., Kotedia, K., Reese, M., Ricketts, S., Halliday, J., Le, C., . . . Carlin, S. (2013). Response of HT29 colorectal xenograft model to cediranib assessed with 18F-fluoromisonidazole positron emission tomography, dynamic contrast-enhanced and diffusion-weighted MRI. NMR in Biomedicine, 26(2), 151-163. doi:https://doi.org/10.1002/nbm.2830
Bollineni, V., Collette, S., & Liu, Y. (2014). Functional and molecular imaging in cancer drug development. Chinese Clinical Oncology , 3(2), 1-9. doi:https://doi.org/10.3978/j.issn.2304-3865.2014.05.05
Byrne, A., Alférez, D., Amant, F., Annibali, D., Arribas, J., Biankin, A., . . . Trusolino, L. (2017). Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nature Reviews Cancer, 17, 254–268. doi:https://doi.org/10.1038/nrc.2016.140
Cekanova, M., & Rathore, K. (2014). Animal models and therapeutic molecular targets of cancer: Utility and limitations. Drug Design, Development and Therapy, 2014(8), 1911—1922. doi:https://doi.org/10.2147/DDDT.S49584
De Plater, L., Laugé, A., Guyader, C., Poupon, M., Assayag, F., De Cremoux, P., . . . Marangoni, E. (2010). Establishment and characterisation of a new breast cancer xenograft obtained from a woman carrying a germline BRCA2 mutation. Br J Cancer , 103, 1192–1200. doi:https://doi.org/10.1038/sj.bjc.6605900
Derose, Y., Wang, G., Lin, Y., Bernard, P., Buys, S., Ebbert, M., . . . Welm, A. (2011). Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nature Medicine, 17, 1514–1520. doi:https://doi.org/10.1038/nm.2454
Fujii, M., Shimokawa, M., Date, S., Takano, A., Matano, M., Nanki, K., . . . Sato, T. (2016). A Colorectal Tumor Organoid Library Demonstrates Progressive Loss of Niche Factor Requirements during Tumorigenesis. Cell Stem Cell, 18(6), 827-838. doi:https://doi.org/10.1016/j.stem.2016.04.003
Gao, H., Korn, J., Ferretti, S., Monahan, J., Wang, Y., Singh, M., . . . Sellers, W. (2015). High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine, 21, 1318–1325. doi:https://doi.org/10.1038/nm.3954
Ghosh, K., Padmanabhan, P., Yang, C., Ng, D., Palanivel, M., Mishra, S., . . . Gulyás, B. (2022). Positron emission tomographic imaging in drug discovery. Drug Discovery Today, 27(1), 280-291. doi:https://doi.org/10.1016/j.drudis.2021.07.025
Glunde, K., & Bhujwalla, Z. M. (2011). Metabolic tumor imaging using magnetic resonance spectroscopy. Seminars in Oncology, 38(1), 26-41. doi:https://doi.org/10.1053/j.seminoncol.2010.11.001
Grisanzio, C., Seeley, A., Chang, M., Collins, M., Di Napoli, A., Cheng, S., . . . Signoretti, S. (2011). Orthotopic xenografts of RCC retain histological, immunophenotypic and genetic features of tumours in patients. The Journal of Pathology, 225(2), 212-221. doi:https://doi.org/10.1002/path.2929
Hammers, H., Verheul, H., Salumbides, B., Sharma, R., Rudek, M., Jaspers, J., . . . Pili, R. (2010). Reversible Epithelial to Mesenchymal Transition and Acquired Resistance to Sunitinib in Patients with Renal Cell Carcinoma: Evidence from a Xenograft Study. Molecular Cancer Therapeutics, 9(6), 1525–1535. doi:https://doi.org/10.1158/1535-7163.MCT-09-1106
Hidalgo, M., Amant, F., Biankin, A., Budinská, E., Byrne, A., Caldas, C., . . . Villanueva, A. (2014). Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research. 4(9), 998–1013. doi:https://doi.org/10.1158/2159-8290.CD-14-0001
Ho, B., Pek, N., & Soh, B. (2018). Disease modeling using 3D organoids derived from human induced pluripotent stem cells. International Journal of Molecular Sciences , 19(4), 936. doi:https://doi.org/10.3390/ijms19040936
Hoff, D., Ramanathan, R., Borad, M., Laheru, D., Smith, L., Wood, T., . . . Hidalgo, M. (2011). Gemcitabine Plus nab-Paclitaxel Is an Active Regimen in Patients With Advanced Pancreatic Cancer: A Phase I/II Trial. Journal of Clinical Oncology, 29(34), 4548-4554. doi:10.1200/JCO.2011.36.5742
Huang, C., Ju, D., Chang, C., Reddy, P., & Velmurugan, B. (2017). A review on the effects of current chemotherapy drugs and natural agents in treating non-small cell lung cancer. BioMedicine, 7(4), 23. doi:https://doi.org/10.1051/bmdcn/2017070423
Institute, N. C. (2020, March 12). www.cancer.gov. Retrieved 2023, from national cancer institute: https://www.cancer.gov/news-events/press-releases/2020/annual-report-nation-2020
Isoldi, M., Visconti, M., & Castrucci, A. (2005). Anti-Cancer Drugs: Molecular Mechanisms of Action. Mini-Reviews in Medicinal Chemistry, 5(7), 685 - 695. doi:https://doi.org/10.2174/1389557054368781
Jacoby, E., Chien, C., & Fry, T. (2014). Murine models of acute leukemia: Important tools in current pediatric leukemia research. Frontiers in Oncology, 4, 95. doi:https://doi.org/10.3389/fonc.2014.00095
Jung, J. (2014). Human tumor xenograft models for preclinical assessment of anticancer drug development . Toxicological Research, 30(1), 1-5. doi:https://doi.org/10.5487/TR.2014.30.1.001
Jung, J., Seol, H., & Chang, S. (2018). The generation and application of patient-derived xenograft model for cancer research. Cancer Research and Treatment , 50(1), 1-10. doi:https://doi.org/10.4143/crt.2017.307
Keunen, O., Johansson, M., Oudin, A., Sanzey, M., Rahim, S., Fack, F., . . . Niclou, S. (2011). Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. PNAS, 108(9), 3749-3754. doi:https://doi.org/10.1073/pnas.1014480108
Kohnken, R., Porcu, P., & Mishra, A. (2017). Overview of the use of murine models in leukemia and lymphoma research . Frontiers in Oncology, 7, 22. doi:https://doi.org/10.3389/fonc.2017.00022
Lee, H., Gaddy, D., Ventura, M., Bernards, N., de Souza, R., Kirpotin, D., . . . Hendriks, B. (2018). Companion Diagnostic 64 Cu-liposome positron emission tomography enables characterization of drug delivery to tumor and predicts response to cancer nanomedicines. Theranostics, 8(9), 2300-2312. doi:10.7150/thno.21670
Lee, J., Mhawech-Fauceglia, P., Lee, N., Parsanian, L., Lin, Y., Gayther, S., & Lawrenson, K. (2013). A three-dimensional microenvironment alters protein expression and chemosensitivity of epithelial ovarian cancer cells in vitro. Laboratory Investigation , 93, 528–542. doi:https://doi.org/10.1038/labinvest.2013.41
Lee, S., Xie, J., & Chen, X. (2010). Peptides and peptide hormones for molecular imaging and disease diagnosis. . Chemical Reviews, 110(5), 3087–3111. doi:https://doi.org/10.1021/cr900361p
Li, Z., Zheng, W., Wang, H., Cheng, Y., Fang, Y., Wu, F., . . . Hui, B. (2021). Application of animal models in cancer research: Recent progress and future prospects. Cancer Management and Research, 2021(13), 2455—2475. doi:https://doi.org/10.2147/CMAR.S302565
Miller, A., Garcia, P., Gamblin, T., Vance, R., & Yoon, K. (2020). Development of gemcitabine-resistant patient-derived xenograft models of pancreatic ductal adenocarcinoma. Cancer Drug Resistance, 3(3). . Cancer Drug Resistance, 3, 572-585. doi:https://doi.org/10.20517/cdr.2020.35
Mollard, S., Ciccolini, J., Imbs, D., El Cheikh, R., Barbolosi, D., & Benzekry, S. (2017). Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis. Oncotarget, 8, 23087-23098. doi:https://doi.org/10.18632/oncotarget.15484
Mullard, A. (2013). Maturing antibody-drug conjugate pipeline hits 30. Nature Reviews Drug Discovery, 12(5), 329–332. doi:https://doi.org/10.1038/nrd4009
Okamoto, A., Asai, T., Hirai, Y., Shimizu, K., Koide, H., Minamino, T., & Oku, N. (2018). Systemic Administration of siRNA with Anti-HB-EGF Antibody-Modified Lipid Nanoparticles for the Treatment of Triple-Negative Breast Cancer . Molecular Pharmaceutics, 15(4), 1495–1504. doi:https://doi.org/10.1021/acs.molpharmaceut.7b01055
Olive, K., Jacobetz, M., Davidson, C., Gopinathan, A., McIntyre, D., Honess, D., . . . Tuveson, D. (2009). Inhibition of Hedgehog Signaling Enhances Delivery of Chemotherapy in a Mouse Model of Pancreatic Cancer. Science, 324(5933), 1457-1461. doi:10.1126/science.1171362
Park, S., Nedrow, J., Josefsson, A., & Sgouros, G. (2017). Human HER2 overexpressing mouse breast cancer cell lines derived from MMTV.f.HuHER2 mice: characterization and use in a model of metastatic breast cancer. Oncotarget, 8(40), 68071-68082. doi:https://doi.org/10.18632/oncotarget.19174
Pignatti, F., Wilking, U., Postmus, D., Wilking, N., Delgado, J., & Bergh, J. (2022). The value of anticancer drugs — a regulatory view. Nature Reviews Clinical Oncology , 19(3), 207–215. doi:https://doi.org/10.1038/s41571-021-00584-z
Poste, G. (2011). Bring on the biomarkers. . Nature, 469(7329), 156–157. doi:https://doi.org/10.1038/469156a
Price, D., Coleman, R., Liao, R., R. C., Polascik, T., & DeGrado, T. R. (2002). Comparison of [18F]Fluorocholine and [18F]Fluorodeoxyglucose for Positron Emission Tomography of Androgen Dependent and Androgen Independent Prostate Cancer. Journal of Urology, 168(1), 273-280. doi:https://doi.org/10.1016/S0022-5347(05)64906-3
Rahman, R., Fonseka, A., Sua, S., Ahmad, M., Rajendran, R., Ambu, S., . . . Chitra, E. (2021). Inhibition of breast cancer xenografts in a mouse model and the induction of apoptosis in multiple breast cancer cell lines by lactoferricin B peptide. Journal of Cellular and Molecular Medicine, 25(15), 7181-7189. doi:10.1111/jcmm.16748
Rongvaux, A., Willinger, T., Martinek, J., Strowig, T., Gearty, S., Teichmann, L., . . . Flavell, R. (2014). Development and function of human innate immune cells in a humanized mouse model. Nature Biotechnology, 32, 364–372. doi:https://doi.org/10.1038/nbt.2858
Schwaederle, M., Zhao, M., Lee, J., Eggermont, A., Schilsky, R., Mendelsohn, J., . . . Kurzrock, R. (2015). Impact of precision medicine in diverse cancers: A meta-analysis of phase II clinical trials. Journal of Clinical Oncology, 33(32), 3817-3825. doi:10.1200/JCO.2015.61.5997
Sharpless, N., & DePinho, R. (2006). The mighty mouse: Genetically engineered mouse models in cancer drug development. Nature Reviews Drug Discovery, 5(9), 741–754. doi:https://doi.org/10.1038/nrd2110
Shiran, M., Amani, D., Ajami, A., Jalalpourroodsari, M., Khalizadeh, M., & Rashidi, M. (2021). Antitumor effects of Auraptene in 4T1 tumor-bearing Balb/c mice. . Hormone Molecular Biology and Clinical Investigation, 43(2), 245-252. doi:https://doi.org/10.1515/hmbci-2020-0090
Shultz, L., Brehm, M., Martinez, J., & Greiner, D. (2012). Humanized mice for immune system investigation: Progress, promise and challenges. Nature Reviews Immunology , 12(11), 786–798. doi: https://doi.org/10.1038/nri3311
Simons, B., & Brayton, C. (2017). Challenges and Limitations of Mouse Xenograft Models of Cancer . Patient Derived Tumor Xenograft Models, 2017, 25–36. doi:https://doi.org/10.1016/B978-0-12-804010-2.00003-5
Siolas, D., & Hannon, G. (2013). Patient-derived tumor xenografts: Transforming clinical samples into mouse models. . Cancer Research, 73(17), 5315–5319. doi:https://doi.org/10.1158/0008-5472.CAN-13-1069
Starke, L., Millward, J., Prinz, C., Sherazi, F., Waiczies, H., Lippert, C., . . . Waiczies, S. (023). First in vivo fluorine-19 magnetic resonance imaging of the multiple sclerosis drug siponimod. Theranostics, 13(4), 1217-1234. doi:10.7150/thno.77041
Sun, H., Cao, S., Mashl, R., Mo, C., Zaccaria, S., Wendl, M., . . . Ding, L. (2021). Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment. Nature Communications, 12, 5086. doi:https://doi.org/10.1038/s41467-021-25177-3
Tentler, J., Tan, A., Weekes, C., Jimeno, A., Leong, S., Pitts, T., . . . Eckhardt, S. (2012). Patient-derived tumour xenografts as models for oncology drug development. Nature Reviews Clinical Oncology, 9(6), 338-350. doi:https://doi.org/10.1038/nrclinonc.2012.61
Threatt, S., Synold, T., Wu, J., & Barton, J. (2020). In vivo anticancer activity of a rhodium metalloinsertor in the HCT116 xenograft tumor model. PNAS, 117(30), 17535–17542. doi:https://doi.org/10.1073/pnas.2006569117
Tian, H., Lyu, Y., Yang, Y., & Hu, Z. (2020). Humanized Rodent Models for Cancer Research. Frontiers in Oncology , 10, 1696. doi: https://doi.org/10.3389/fonc.2020.01696
Wang, J., Miletic, H., Sakariassen, P. .., Huszthy, P., Jacobsen, H., Brekkå, N., . . . Enger, P. (2009). A reproducible brain tumour model established from human glioblastoma biopsies. BMC Cancer, 9, 465. doi:https://doi.org/10.1186/1471-2407-9-465
Wang, Y., Revelo, M., Sudilovsky, D., Cao, M., Chen, W., Goetz, L., . . . Hayward, S. (2005). Development and characterization of efficient xenograft models for benign and malignant human prostate tissue. The Prostate, 64(2), 149-159. doi: https://doi.org/10.1002/pros.20225
Wen, Y., Bai, H., Zhu, J., Song, X., Tang, G., & Li, J. (2020). A supramolecular platform for controlling and optimizing molecular architectures of siRNA targeted delivery vehicles . Sci Adv, 6(31), eabc2148. doi:10.1126/sciadv.abc2148
Xie, J., & Lin, Y. (2020). Patient-derived xenograft models for personalized medicine in colorectal cancer . Clinical and Experimental Medicine , 20(2), 167–172. doi:https://doi.org/10.1007/s10238-020-00609-4
Yoshida, G. (2020). Applications of patient-derived tumor xenograft models and tumor organoids. Journal of Hematology and Oncology, 13(1), 4. doi:https://doi.org/10.1186/s13045-019-0829-z
Yoshida, T., Kinoshita, H., Segawa, T., Nakamura, E., Inoue, T., Shimizu, Y., . . . Ogawa, O. (2005). Antiandrogen bicalutamide promotes tumor growth in a novel androgen-dependent prostate cancer xenograft model derived from a bicalutamide-treated patient. Cancer Research, 65(21), 9611–9616. doi:https://doi.org/10.1158/0008-5472.CAN-05-0817
Zhang, J., & Liu, J. (2013). Tumor stroma as targets for cancer therapy. Pharmacology and Therapeutics, 137(2), 200-215. doi:https://doi.org/10.1016/j.pharmthera.2012.10.003
Zhu, Y., Kang, E., Wilson, M., Basso, T., Chen, E., Yu, Y., & Li, Y. (2022). 3D Tumor Spheroid and Organoid to Model Tumor Microenvironment for Cancer Immunotherapy. Organoids,, 1(2), 149-167. doi:https://doi.org/10.3390/organoids1020012
American Cancer Society. (2018). Cancer Facts & Figures 2018. Atlanta: American Cancer Society.
Bachelard, C., Coquan, E., du Rusquec, P., Paoletti, X., & Le Tourneau, C. (2021). Risks and benefits of anticancer drugs in advanced cancer patients: A systematic review and meta-analysis. EClinicalMedicine, 40(2021), 101130. doi:https://doi.org/10.1016/j.eclinm.2021.101130
Bokacheva, L., Kotedia, K., Reese, M., Ricketts, S., Halliday, J., Le, C., . . . Carlin, S. (2013). Response of HT29 colorectal xenograft model to cediranib assessed with 18F-fluoromisonidazole positron emission tomography, dynamic contrast-enhanced and diffusion-weighted MRI. NMR in Biomedicine, 26(2), 151-163. doi:https://doi.org/10.1002/nbm.2830
Bollineni, V., Collette, S., & Liu, Y. (2014). Functional and molecular imaging in cancer drug development. Chinese Clinical Oncology , 3(2), 1-9. doi:https://doi.org/10.3978/j.issn.2304-3865.2014.05.05
Byrne, A., Alférez, D., Amant, F., Annibali, D., Arribas, J., Biankin, A., . . . Trusolino, L. (2017). Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nature Reviews Cancer, 17, 254–268. doi:https://doi.org/10.1038/nrc.2016.140
Cekanova, M., & Rathore, K. (2014). Animal models and therapeutic molecular targets of cancer: Utility and limitations. Drug Design, Development and Therapy, 2014(8), 1911—1922. doi:https://doi.org/10.2147/DDDT.S49584
De Plater, L., Laugé, A., Guyader, C., Poupon, M., Assayag, F., De Cremoux, P., . . . Marangoni, E. (2010). Establishment and characterisation of a new breast cancer xenograft obtained from a woman carrying a germline BRCA2 mutation. Br J Cancer , 103, 1192–1200. doi:https://doi.org/10.1038/sj.bjc.6605900
Derose, Y., Wang, G., Lin, Y., Bernard, P., Buys, S., Ebbert, M., . . . Welm, A. (2011). Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nature Medicine, 17, 1514–1520. doi:https://doi.org/10.1038/nm.2454
Fujii, M., Shimokawa, M., Date, S., Takano, A., Matano, M., Nanki, K., . . . Sato, T. (2016). A Colorectal Tumor Organoid Library Demonstrates Progressive Loss of Niche Factor Requirements during Tumorigenesis. Cell Stem Cell, 18(6), 827-838. doi:https://doi.org/10.1016/j.stem.2016.04.003
Gao, H., Korn, J., Ferretti, S., Monahan, J., Wang, Y., Singh, M., . . . Sellers, W. (2015). High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine, 21, 1318–1325. doi:https://doi.org/10.1038/nm.3954
Ghosh, K., Padmanabhan, P., Yang, C., Ng, D., Palanivel, M., Mishra, S., . . . Gulyás, B. (2022). Positron emission tomographic imaging in drug discovery. Drug Discovery Today, 27(1), 280-291. doi:https://doi.org/10.1016/j.drudis.2021.07.025
Glunde, K., & Bhujwalla, Z. M. (2011). Metabolic tumor imaging using magnetic resonance spectroscopy. Seminars in Oncology, 38(1), 26-41. doi:https://doi.org/10.1053/j.seminoncol.2010.11.001
Grisanzio, C., Seeley, A., Chang, M., Collins, M., Di Napoli, A., Cheng, S., . . . Signoretti, S. (2011). Orthotopic xenografts of RCC retain histological, immunophenotypic and genetic features of tumours in patients. The Journal of Pathology, 225(2), 212-221. doi:https://doi.org/10.1002/path.2929
Hammers, H., Verheul, H., Salumbides, B., Sharma, R., Rudek, M., Jaspers, J., . . . Pili, R. (2010). Reversible Epithelial to Mesenchymal Transition and Acquired Resistance to Sunitinib in Patients with Renal Cell Carcinoma: Evidence from a Xenograft Study. Molecular Cancer Therapeutics, 9(6), 1525–1535. doi:https://doi.org/10.1158/1535-7163.MCT-09-1106
Hidalgo, M., Amant, F., Biankin, A., Budinská, E., Byrne, A., Caldas, C., . . . Villanueva, A. (2014). Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research. 4(9), 998–1013. doi:https://doi.org/10.1158/2159-8290.CD-14-0001
Ho, B., Pek, N., & Soh, B. (2018). Disease modeling using 3D organoids derived from human induced pluripotent stem cells. International Journal of Molecular Sciences , 19(4), 936. doi:https://doi.org/10.3390/ijms19040936
Hoff, D., Ramanathan, R., Borad, M., Laheru, D., Smith, L., Wood, T., . . . Hidalgo, M. (2011). Gemcitabine Plus nab-Paclitaxel Is an Active Regimen in Patients With Advanced Pancreatic Cancer: A Phase I/II Trial. Journal of Clinical Oncology, 29(34), 4548-4554. doi:10.1200/JCO.2011.36.5742
Huang, C., Ju, D., Chang, C., Reddy, P., & Velmurugan, B. (2017). A review on the effects of current chemotherapy drugs and natural agents in treating non-small cell lung cancer. BioMedicine, 7(4), 23. doi:https://doi.org/10.1051/bmdcn/2017070423
Institute, N. C. (2020, March 12). www.cancer.gov. Retrieved 2023, from national cancer institute: https://www.cancer.gov/news-events/press-releases/2020/annual-report-nation-2020
Isoldi, M., Visconti, M., & Castrucci, A. (2005). Anti-Cancer Drugs: Molecular Mechanisms of Action. Mini-Reviews in Medicinal Chemistry, 5(7), 685 - 695. doi:https://doi.org/10.2174/1389557054368781
Jacoby, E., Chien, C., & Fry, T. (2014). Murine models of acute leukemia: Important tools in current pediatric leukemia research. Frontiers in Oncology, 4, 95. doi:https://doi.org/10.3389/fonc.2014.00095
Jung, J. (2014). Human tumor xenograft models for preclinical assessment of anticancer drug development . Toxicological Research, 30(1), 1-5. doi:https://doi.org/10.5487/TR.2014.30.1.001
Jung, J., Seol, H., & Chang, S. (2018). The generation and application of patient-derived xenograft model for cancer research. Cancer Research and Treatment , 50(1), 1-10. doi:https://doi.org/10.4143/crt.2017.307
Keunen, O., Johansson, M., Oudin, A., Sanzey, M., Rahim, S., Fack, F., . . . Niclou, S. (2011). Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. PNAS, 108(9), 3749-3754. doi:https://doi.org/10.1073/pnas.1014480108
Kohnken, R., Porcu, P., & Mishra, A. (2017). Overview of the use of murine models in leukemia and lymphoma research . Frontiers in Oncology, 7, 22. doi:https://doi.org/10.3389/fonc.2017.00022
Lee, H., Gaddy, D., Ventura, M., Bernards, N., de Souza, R., Kirpotin, D., . . . Hendriks, B. (2018). Companion Diagnostic 64 Cu-liposome positron emission tomography enables characterization of drug delivery to tumor and predicts response to cancer nanomedicines. Theranostics, 8(9), 2300-2312. doi:10.7150/thno.21670
Lee, J., Mhawech-Fauceglia, P., Lee, N., Parsanian, L., Lin, Y., Gayther, S., & Lawrenson, K. (2013). A three-dimensional microenvironment alters protein expression and chemosensitivity of epithelial ovarian cancer cells in vitro. Laboratory Investigation , 93, 528–542. doi:https://doi.org/10.1038/labinvest.2013.41
Lee, S., Xie, J., & Chen, X. (2010). Peptides and peptide hormones for molecular imaging and disease diagnosis. . Chemical Reviews, 110(5), 3087–3111. doi:https://doi.org/10.1021/cr900361p
Li, Z., Zheng, W., Wang, H., Cheng, Y., Fang, Y., Wu, F., . . . Hui, B. (2021). Application of animal models in cancer research: Recent progress and future prospects. Cancer Management and Research, 2021(13), 2455—2475. doi:https://doi.org/10.2147/CMAR.S302565
Miller, A., Garcia, P., Gamblin, T., Vance, R., & Yoon, K. (2020). Development of gemcitabine-resistant patient-derived xenograft models of pancreatic ductal adenocarcinoma. Cancer Drug Resistance, 3(3). . Cancer Drug Resistance, 3, 572-585. doi:https://doi.org/10.20517/cdr.2020.35
Mollard, S., Ciccolini, J., Imbs, D., El Cheikh, R., Barbolosi, D., & Benzekry, S. (2017). Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis. Oncotarget, 8, 23087-23098. doi:https://doi.org/10.18632/oncotarget.15484
Mullard, A. (2013). Maturing antibody-drug conjugate pipeline hits 30. Nature Reviews Drug Discovery, 12(5), 329–332. doi:https://doi.org/10.1038/nrd4009
Okamoto, A., Asai, T., Hirai, Y., Shimizu, K., Koide, H., Minamino, T., & Oku, N. (2018). Systemic Administration of siRNA with Anti-HB-EGF Antibody-Modified Lipid Nanoparticles for the Treatment of Triple-Negative Breast Cancer . Molecular Pharmaceutics, 15(4), 1495–1504. doi:https://doi.org/10.1021/acs.molpharmaceut.7b01055
Olive, K., Jacobetz, M., Davidson, C., Gopinathan, A., McIntyre, D., Honess, D., . . . Tuveson, D. (2009). Inhibition of Hedgehog Signaling Enhances Delivery of Chemotherapy in a Mouse Model of Pancreatic Cancer. Science, 324(5933), 1457-1461. doi:10.1126/science.1171362
Park, S., Nedrow, J., Josefsson, A., & Sgouros, G. (2017). Human HER2 overexpressing mouse breast cancer cell lines derived from MMTV.f.HuHER2 mice: characterization and use in a model of metastatic breast cancer. Oncotarget, 8(40), 68071-68082. doi:https://doi.org/10.18632/oncotarget.19174
Pignatti, F., Wilking, U., Postmus, D., Wilking, N., Delgado, J., & Bergh, J. (2022). The value of anticancer drugs — a regulatory view. Nature Reviews Clinical Oncology , 19(3), 207–215. doi:https://doi.org/10.1038/s41571-021-00584-z
Poste, G. (2011). Bring on the biomarkers. . Nature, 469(7329), 156–157. doi:https://doi.org/10.1038/469156a
Price, D., Coleman, R., Liao, R., R. C., Polascik, T., & DeGrado, T. R. (2002). Comparison of [18F]Fluorocholine and [18F]Fluorodeoxyglucose for Positron Emission Tomography of Androgen Dependent and Androgen Independent Prostate Cancer. Journal of Urology, 168(1), 273-280. doi:https://doi.org/10.1016/S0022-5347(05)64906-3
Rahman, R., Fonseka, A., Sua, S., Ahmad, M., Rajendran, R., Ambu, S., . . . Chitra, E. (2021). Inhibition of breast cancer xenografts in a mouse model and the induction of apoptosis in multiple breast cancer cell lines by lactoferricin B peptide. Journal of Cellular and Molecular Medicine, 25(15), 7181-7189. doi:10.1111/jcmm.16748
Rongvaux, A., Willinger, T., Martinek, J., Strowig, T., Gearty, S., Teichmann, L., . . . Flavell, R. (2014). Development and function of human innate immune cells in a humanized mouse model. Nature Biotechnology, 32, 364–372. doi:https://doi.org/10.1038/nbt.2858
Schwaederle, M., Zhao, M., Lee, J., Eggermont, A., Schilsky, R., Mendelsohn, J., . . . Kurzrock, R. (2015). Impact of precision medicine in diverse cancers: A meta-analysis of phase II clinical trials. Journal of Clinical Oncology, 33(32), 3817-3825. doi:10.1200/JCO.2015.61.5997
Sharpless, N., & DePinho, R. (2006). The mighty mouse: Genetically engineered mouse models in cancer drug development. Nature Reviews Drug Discovery, 5(9), 741–754. doi:https://doi.org/10.1038/nrd2110
Shiran, M., Amani, D., Ajami, A., Jalalpourroodsari, M., Khalizadeh, M., & Rashidi, M. (2021). Antitumor effects of Auraptene in 4T1 tumor-bearing Balb/c mice. . Hormone Molecular Biology and Clinical Investigation, 43(2), 245-252. doi:https://doi.org/10.1515/hmbci-2020-0090
Shultz, L., Brehm, M., Martinez, J., & Greiner, D. (2012). Humanized mice for immune system investigation: Progress, promise and challenges. Nature Reviews Immunology , 12(11), 786–798. doi: https://doi.org/10.1038/nri3311
Simons, B., & Brayton, C. (2017). Challenges and Limitations of Mouse Xenograft Models of Cancer . Patient Derived Tumor Xenograft Models, 2017, 25–36. doi:https://doi.org/10.1016/B978-0-12-804010-2.00003-5
Siolas, D., & Hannon, G. (2013). Patient-derived tumor xenografts: Transforming clinical samples into mouse models. . Cancer Research, 73(17), 5315–5319. doi:https://doi.org/10.1158/0008-5472.CAN-13-1069
Starke, L., Millward, J., Prinz, C., Sherazi, F., Waiczies, H., Lippert, C., . . . Waiczies, S. (023). First in vivo fluorine-19 magnetic resonance imaging of the multiple sclerosis drug siponimod. Theranostics, 13(4), 1217-1234. doi:10.7150/thno.77041
Sun, H., Cao, S., Mashl, R., Mo, C., Zaccaria, S., Wendl, M., . . . Ding, L. (2021). Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment. Nature Communications, 12, 5086. doi:https://doi.org/10.1038/s41467-021-25177-3
Tentler, J., Tan, A., Weekes, C., Jimeno, A., Leong, S., Pitts, T., . . . Eckhardt, S. (2012). Patient-derived tumour xenografts as models for oncology drug development. Nature Reviews Clinical Oncology, 9(6), 338-350. doi:https://doi.org/10.1038/nrclinonc.2012.61
Threatt, S., Synold, T., Wu, J., & Barton, J. (2020). In vivo anticancer activity of a rhodium metalloinsertor in the HCT116 xenograft tumor model. PNAS, 117(30), 17535–17542. doi:https://doi.org/10.1073/pnas.2006569117
Tian, H., Lyu, Y., Yang, Y., & Hu, Z. (2020). Humanized Rodent Models for Cancer Research. Frontiers in Oncology , 10, 1696. doi: https://doi.org/10.3389/fonc.2020.01696
Wang, J., Miletic, H., Sakariassen, P. .., Huszthy, P., Jacobsen, H., Brekkå, N., . . . Enger, P. (2009). A reproducible brain tumour model established from human glioblastoma biopsies. BMC Cancer, 9, 465. doi:https://doi.org/10.1186/1471-2407-9-465
Wang, Y., Revelo, M., Sudilovsky, D., Cao, M., Chen, W., Goetz, L., . . . Hayward, S. (2005). Development and characterization of efficient xenograft models for benign and malignant human prostate tissue. The Prostate, 64(2), 149-159. doi: https://doi.org/10.1002/pros.20225
Wen, Y., Bai, H., Zhu, J., Song, X., Tang, G., & Li, J. (2020). A supramolecular platform for controlling and optimizing molecular architectures of siRNA targeted delivery vehicles . Sci Adv, 6(31), eabc2148. doi:10.1126/sciadv.abc2148
Xie, J., & Lin, Y. (2020). Patient-derived xenograft models for personalized medicine in colorectal cancer . Clinical and Experimental Medicine , 20(2), 167–172. doi:https://doi.org/10.1007/s10238-020-00609-4
Yoshida, G. (2020). Applications of patient-derived tumor xenograft models and tumor organoids. Journal of Hematology and Oncology, 13(1), 4. doi:https://doi.org/10.1186/s13045-019-0829-z
Yoshida, T., Kinoshita, H., Segawa, T., Nakamura, E., Inoue, T., Shimizu, Y., . . . Ogawa, O. (2005). Antiandrogen bicalutamide promotes tumor growth in a novel androgen-dependent prostate cancer xenograft model derived from a bicalutamide-treated patient. Cancer Research, 65(21), 9611–9616. doi:https://doi.org/10.1158/0008-5472.CAN-05-0817
Zhang, J., & Liu, J. (2013). Tumor stroma as targets for cancer therapy. Pharmacology and Therapeutics, 137(2), 200-215. doi:https://doi.org/10.1016/j.pharmthera.2012.10.003
Zhu, Y., Kang, E., Wilson, M., Basso, T., Chen, E., Yu, Y., & Li, Y. (2022). 3D Tumor Spheroid and Organoid to Model Tumor Microenvironment for Cancer Immunotherapy. Organoids,, 1(2), 149-167. doi:https://doi.org/10.3390/organoids1020012
Published
2023-07-10
How to Cite
sadaqa, E., & Muhammad Ikhlas Arsul. (2023). Xenograft Models for Preclinical Assessment of Anticancer Therapies: A Comprehensive Review. Ad-Dawaa’ Journal of Pharmaceutical Sciences, 6(1), 1-16. https://doi.org/10.24252/djps.v6i1.38677
Issue
Section
Artikel
Once an article was published in the journal, the author(s) are:
- granted to the journal right licensed under Creative Commons License Attribution that allows others to share the work with an acknowledgement of the work's authorship.
- permitted to publish their work online in third parties as it can lead to wider dissemination of the work.
- continue to be the copyright owner and allow the journal to publish the article with the CC BY-SA license
- receiving a DOI (Digital Object Identifier) of the work.
1.png)